| must vs. prob | |
| Total N = 14 | |
| Bin | Count |
|---|---|
| must vs. will | |
| Total N = 6 | |
| Bin | Count |
|---|---|
| haveto vs. must | |
| Total N = 9 | |
| Bin | Count |
|---|---|
| bare vs. must | |
| Total N = 11 | |
| Bin | Count |
|---|---|
Utku and Sarah
| must vs. prob | |
| Total N = 14 | |
| Bin | Count |
|---|---|
| [0.5,0.6) | 3 |
| [0.7,0.8) | 6 |
| [0.9,1] | 5 |
| must vs. will | |
| Total N = 6 | |
| Bin | Count |
|---|---|
| [0.7,0.8) | 1 |
| [0.9,1] | 5 |
| haveto vs. must | |
| Total N = 9 | |
| Bin | Count |
|---|---|
| [0.7,0.8) | 2 |
| [0.9,1] | 7 |
| bare vs. must | |
| Total N = 11 | |
| Bin | Count |
|---|---|
| [0.9,1] | 11 |
Mean Age: 22.00 (18–49)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[51] | 18 | Male | Maryland, United States of America | Mac | English | Spanish (proficient), Darija (novice) |
| S[55] | 30 | Male | MD, USA | Mac | English |
|
| S[60] | 18 | Unlabeled | Maryland, United States | PC (Lenovo) | English | Cantonese |
| S[5] | 19 | Female | Maryland, USA | PC | English | Hindi, Urdu |
| S[6] | 18 | Female | MD, USA | Windows | English |
|
| S[9] | 21 | Female | Maryland, USA | Mac | English |
|
| S[12] | 49 | MALE | MARYLAND, USA | LINUX PC | ENGLISH | SPANISH, ITALIAN, RUSSIAN NONE FLUENTLY |
| S[17] | 19 | Female | Maryland, United States | Windows Laptop | English | Spanish |
| S[21] | 18 | Male | Maryland | Laptop | English |
|
| S[22] | 18 | Male | Maryland, United States | PC | English |
|
| S[26] | 18 | Female | Maryland, USA | Lenovo | English |
|
| S[27] | 20 | Female | Maryland, USA | Windows | English | Mandarin Chinese |
| S[31] | 18 | Female | College Park, Maryland | Mac | English |
|
| S[34] | 24 | Female | Maryland, United States | Mac | English | Korean |
Mean Age: 21.67 (18–36)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[53] | 18 | male | maryland, usa | pc | english |
|
| S[57] | 18 | female | Maryland, USA | laptop | english |
|
| S[59] | 18 | Female | Maryland, USA | Mac | English | Spanish |
| S[10] | 21 | Male | Maryland, Prince George’s County | PC | English | Spanish |
| S[11] | 19 | Male | Maryland, US | Windows | English |
|
| S[20] | 36 | Male | Baltimore, MD | Mac | English | French, German |
Mean Age: 19.00 (18–22)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[56] | 18 | female | Maryland, USA | lenovo thinkpad | english | chinese (beginner) |
| S[58] | 19 | Female | MD, USA | Mac | English |
|
| S[1] | 18 | Female | College Park, Maryland | Mac | English | Cantonese |
| S[13] | 19 | Male | College Park, MD, USA | Mac | English | Gujarati |
| S[15] | 20 | Male | College Park, MD | Mac | English | Telugu |
| S[16] | 22 | Male | MD, USA | PC | English | German |
| S[28] | 18 | Female | Maryland, USA | Mac | English | Spanish |
| S[30] | 19 | Male | Maryland, United States of America | PC | English |
|
| S[35] | 18 | Female | College Park, MD | Mac | English | Mandarin Chinese |
Mean Age: 21.27 (18–40)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[52] | 18 | male | MD, USA | Thinkpad X1 Carbon | English | Mandarin, Spanish, Japanese, Hindi |
| S[54] | 20 | Female | Maryland, United States | PC | English |
|
| S[3] | 20 | Male | Massachusetts | PC | English |
|
| S[7] | 18 | woman | MD, USA | Windows | English | Japanese |
| S[8] | 40 | Female | Maryland, USA | PC | English |
|
| S[14] | 19 | Female | Maryland, United States | Mac | English | Vietnamese |
| S[18] | 26 | male | Maryland, USA | PC | English | Spanish (L2 ~B2/C1) |
| S[19] | 19 | Female | Maryland, USA | PC | English | Spanish |
| S[24] | 18 | Female | Maryland, United States | Mac | English |
|
| S[25] | 18 | female | Maryland, USA | Dell laptop | English | Vietnamese |
| S[32] | 18 | female | MD, United States | Mac | English |
|
We report target-response proportions by inference (a, d, i) and type (must, prob, have to, will, bare). Confidence intervals are Morey–Cousineau, truncated to [0,1].
Per-cell trials and items increase across blocks:
Block: prob (n = 10; N = 5)
Takeaway: Strong complementarity—prob peaks in a, must in d; i is mixed with wider CIs.
Block: will (n = 10; N = 5)
Takeaway: Will is at (or near) floor; must dominates, with a small will share in d/i.
Block: have to (n = 14; N = 7)
Takeaway: Must is clearly preferred; i shows a modest but reliable share for have to.
Block: bare (n = 22; N = 11)
Takeaway: Must overwhelmingly beats bare across all inference conditions; bare shows a small baseline.
| must vs. prob | |
| Total N = 21 | |
| Bin | Count |
|---|---|
| [0.5,0.6) | 1 |
| [0.9,1] | 20 |
| must vs. will | |
| Total N = 21 | |
| Bin | Count |
|---|---|
| [0.7,0.8) | 1 |
| [0.9,1] | 20 |
| haveto vs. must | |
| Total N = 21 | |
| Bin | Count |
|---|---|
| [0.9,1] | 21 |
| bare vs. must | |
| Total N = 21 | |
| Bin | Count |
|---|---|
| [0.7,0.8) | 3 |
| [0.9,1] | 18 |
Mean Age: 45.86 (27–73)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[1] | 44 | male | TN, USA | HP PC | English |
|
| S[2] | 49 | male | Michigan, USA | PC | English |
|
| S[3] | 46 | male | texas, USA | PC | English |
|
| S[4] | 52 | male | Tennessee, USA | Mac | English |
|
| S[5] | 32 | Male | NC, USA | PC | English |
|
| S[6] | 59 | Female | Alabama | P.C. | English |
|
| S[7] | 73 | Female | Rhode Island | PC | English |
|
| S[8] | 47 | Male | NY, USA | PC | English |
|
| S[9] | 41 | male | MA, USA | PC | English |
|
| S[10] | 48 | Woman | NY, USA | PC | English |
|
| S[11] | 56 | male | Virginia, USA | PC | English |
|
| S[12] | 50 | female | California, United States | laptop | English |
|
| S[13] | 64 | Male | Illinois, United States | PC | English |
|
| S[14] | 30 | Female | Hawaii, USA | PC | English |
|
| S[15] | 34 | Female | New York, United States of America | PC | English |
|
| S[16] | 40 | Female | New York, USA | Laptop | English |
|
| S[17] | 42 | Male | Kentucky, USA | Windows laptop | English |
|
| S[18] | 37 | male | Indiana, United states | PC | English |
|
| S[19] | 27 | woman | US, TEXAS | WINDOWS | ENGLISH |
|
| S[20] | 32 | man | Tennessee, United States | PC | English | Spanish |
| S[21] | 60 | Male | North Carolina, United States | PC | English |
|
Mean Age: 46.10 (26–67)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[1] | 31 | female | NJ USA | Mac | English |
|
| S[2] | 52 | Female | Minnesota, USA | PC | English | Beginning Spanish |
| S[3] | 54 | Male | CA, USA | PC | English |
|
| S[4] | 30 | Female | Kentucky, USA | PC | English |
|
| S[5] | 43 | female | Connecticut, USA | PC | English |
|
| S[6] | 26 | male | north carolina, united states | pc | english |
|
| S[7] | 55 | female | Tennessee, USA | Windows Laptop | English |
|
| S[8] | 37 | Female | Georgia, United States | PC | English |
|
| S[9] | 36 | female | Maryland United states | Chromebook | English |
|
| S[10] | 43 | Female | Texas, USA | Mac | English |
|
| S[11] | 67 | female | Wisconsin, USA | PC | English | Spanish |
| S[12] | 46 | male | tn, usa | PC | English |
|
| S[13] | 49 | Female | Massachusetts | PC | English |
|
| S[14] | 56 | female | california united states | pc | english |
|
| S[15] | 62 | male | ny, usa | pc | English |
|
| S[16] | 51 | Female | Virginia | Laptop | English |
|
| S[17] | 28 | Female | Maine, USA | PC | English |
|
| S[18] | 59 | male | Tennessee, USA | PC | English |
|
| S[19] | 52 | female | Virginia | PC | English | English |
| S[20] | 29 | Female | California United States | Laptop | English |
|
| S[21] | 62 | female | Washington, USA | PC | English |
|
Mean Age: 37.24 (22–71)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[1] | 24 | Male | Florida, USA | PC | English |
|
| S[2] | 45 | Male | Oregon, USA | PC | English |
|
| S[3] | 29 | f | fl, usa | pc | english |
|
| S[4] | 22 | F | CO, USA | PC | ENGLISH |
|
| S[5] | 32 | male | Minnesota, USA | PC | English |
|
| S[6] | 27 | female | Georgia, USA | PC | English |
|
| S[7] | 38 | Female | Louisiana, United States | PC | English |
|
| S[8] | 33 | Woman | Maryland, USA | Mac | English |
|
| S[9] | 58 | Male | Georgia, USA | PC | English |
|
| S[10] | 45 | Male | MS, USA | MAC | English |
|
| S[11] | 53 | Female | United States | Mac | English | English |
| S[12] | 35 | Non-Binary AFAB | MN, USA | Mac | English |
|
| S[13] | 42 | woman | Tennessee, USA | PC(windows) | English |
|
| S[14] | 30 | Female | Indiana, USA | PC | English |
|
| S[15] | 41 | Female | California, USA | Mac | English |
|
| S[16] | 28 | Male | PA, USA | Linux | English |
|
| S[17] | 47 | Male | Michigan, United States | PC | English |
|
| S[18] | 71 | Female | Arizona, United States | PC | English |
|
| S[19] | 26 | Female | Illinois, USA | PC | English Language |
|
| S[20] | 25 | Woman | Delaware | PC | English |
|
| S[21] | 31 | Male | MA, USA | Windows | English |
|
Mean Age: 43.00 (21–66)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[1] | 36 | male | CA, USA | PC | English |
|
| S[2] | 35 | Male | AL, Geneva | Chromebook | English | No others |
| S[3] | 38 | male | Michigan, United States Of America | PC | English |
|
| S[4] | 55 | male | California, USA | PC | English |
|
| S[5] | 51 | Male | New York, United States | PC | English |
|
| S[6] | 61 | female | California, US | PC | English |
|
| S[7] | 21 | male | Georgia, United States | PC | English |
|
| S[8] | 35 | male | VA, USA | windows PC | English |
|
| S[9] | 45 | Male | Ohio, United States | PC | English |
|
| S[10] | 66 | Female | Florida, United States | PC | English |
|
| S[11] | 44 | Male | GA, USA | PC | English |
|
| S[12] | 40 | Female | Louisiana, United States | PC | English |
|
| S[13] | 25 | Male | United States | PC | English |
|
| S[14] | 47 | male | New York, United States | PC | English |
|
| S[15] | 46 | Female | Nevada, USA | PC | English |
|
| S[16] | 47 | Female | Mississippi, United States | PC | English |
|
| S[17] | 43 | Woman | SC, United States | Windows | English |
|
| S[18] | 29 | female | ohio, USA | PC | english |
|
| S[19] | 39 | Male | United States | PC | English |
|
| S[20] | 45 | Male | Indiana, USA | Laptop | English |
|
| S[21] | 55 | female | NY | PC | English | English |
We report target-response proportions by inference (a, d, i) and type (must, prob, have to, will, bare). Confidence intervals are Morey–Cousineau, truncated to [0,1].
Per-cell trials and items by block:
Block: prob (n ≈ 40; N = 20)
Takeaway: Clear complementarity—prob peaks in a, must in d; i is near 50/50.
Block: will (n = 40; N = 20)
Takeaway: Must dominates; will contributes small shares, largest in d.
Block: have to (n = 42; N = 21)
Takeaway: Must is preferred overall; d shows the largest have to share.
Block: bare (n = 36; N = 18)
Takeaway: Unlike Exp 1, bare takes a substantial share—especially in d—though must still leads overall.
The results does not seem to be a function of age. But there are some mismatches.
| haveto vs. must | |
| Total N = 21 | |
| Bin | Count |
|---|---|
| [0.9,1] | 21 |
| bare vs. must | |
| Total N = 21 | |
| Bin | Count |
|---|---|
| [0.7,0.8) | 1 |
| [0.9,1] | 20 |
Mean Age: 48.57 (35–73)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[1] | 37 | male | California, USA | PC | english |
|
| S[2] | 60 | Female | Idaho, USA | PC | English |
|
| S[3] | 47 | MALE | BURKETVILLE, MD | PC | ENGLISH | NONE |
| S[4] | 38 | male | ma, usa | mac | english |
|
| S[5] | 60 | Female | CA, USA | Mac | English |
|
| S[6] | 50 | Female | California, USA | Mac | English |
|
| S[7] | 42 | female | RI, USA | PC | English |
|
| S[8] | 73 | Female | Kansas, USA | laptop | English |
|
| S[9] | 35 | male | Colorado, United States | PC | English |
|
| S[10] | 49 | Male | MN | Mac | English |
|
| S[11] | 46 | male | Washington, USA | pc | English |
|
| S[12] | 42 | Female | Virginia, USA | PC | English |
|
| S[13] | 72 | female | Ohio, United States | PC | English |
|
| S[14] | 44 | female | IL, USA | PC | English |
|
| S[15] | 46 | Male | Michigan, United States | PC | English |
|
| S[16] | 52 | male | CT USA | pc | English |
|
| S[17] | 41 | male | Indiana. United states | Laptop | English |
|
| S[18] | 58 | female | Michigan, United States | PC | English |
|
| S[19] | 39 | male | NH, USA | PC | English |
|
| S[20] | 44 | female | California, US | 1pc | English |
|
| S[21] | 45 | Male | NC, USA | PC | English | French (Intermediate), Spanish (Basic) |
Mean Age: 48.52 (36–72)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[1] | 49 | woman | NY, USA | PC | English |
|
| S[2] | 43 | Female | New York, US | HP | English |
|
| S[3] | 38 | Female | New York, United States | PC | English |
|
| S[4] | 38 | Male | Arlington, USA | PC | English | English |
| S[5] | 71 | Male | Texas, United States | PC | English |
|
| S[6] | 43 | F | MA, USA | Mac | English |
|
| S[7] | 45 | Female | Maryland, USA | Laptop%2C Windows | English |
|
| S[8] | 37 | female | La Porte, Texas | PC | English |
|
| S[9] | 47 | Male | Florida, USA | PC | English |
|
| S[10] | 52 | female | Missouri, USA | Mac | English |
|
| S[11] | 51 | male | New Mexico, USA | PC | English |
|
| S[12] | 44 | Female | NJ, United States | PC | English |
|
| S[13] | 51 | Male | Nevada, USA | PC | English | 0 |
| S[14] | 48 | male | OH, USA | PC | English |
|
| S[15] | 36 | female | California, usa | pc | english |
|
| S[16] | 72 | Female | Louisiana, United States | PC | English |
|
| S[17] | 57 | woman | CA USA | Mac | English |
|
| S[18] | 51 | female | michigan | pc | english |
|
| S[19] | 60 | male | Missouri, USA | PC | American English | Patios |
| S[20] | 39 | male | RI, USA | Pc | English |
|
| S[21] | 47 | Male | PA, USA | MAC | English |
|
We report target-response proportions by inference (a, d, i) and type (must, prob, have to, will, bare). Confidence intervals are Morey–Cousineau, truncated to [0,1].
Per-cell trials and items by block:
Block: have to (n = 42; N = 21)
Takeaway: Must clearly dominates; have to contributes a small but non-zero share, largest in i.
Block: bare (n = 40; N = 20)
Takeaway: Bare claims a substantial minority share—especially in d—though must remains the plurality across inference conditions.
| gotta vs. must | |
| Total N = 21 | |
| Bin | Count |
|---|---|
| [0.7,0.8) | 2 |
| [0.9,1] | 19 |
| gotta vs. must | |
| Total N = 21 | |
| Bin | Count |
|---|---|
| [0.7,0.8) | 2 |
| [0.9,1] | 19 |
Mean Age: 46.00 (35–65)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[1] | 65 | female | Arkansas, USA | Windows | English |
|
| S[2] | 42 | female | MO, USA | Mac | English |
|
| S[3] | 51 | Female | California, USA | PC | English |
|
| S[4] | 62 | male | florida, usa | pc | english |
|
| S[5] | 43 | Female | Kansas, USA | PC | English |
|
| S[6] | 51 | female | IL, United States | PC | English |
|
| S[7] | 35 | Female | OH, United States | PC | English |
|
| S[8] | 50 | male | Texas, USA | pc | English | Spanish |
| S[9] | 39 | female | Willis, TX | PC | English |
|
| S[10] | 39 | Male | Wyoming, USA | PC | English |
|
| S[11] | 50 | male | New Jersey, USA | PC | English |
|
| S[12] | 38 | female | Oklahoma, USA | PC | English |
|
| S[13] | 37 | Male | IL, USA | PC | English |
|
| S[14] | 37 | Male | New Jersey; United States | Desktop PC | English |
|
| S[15] | 54 | Female | Illinois, United States | Laptop | English |
|
| S[16] | 48 | Male | NH | PC (windows) | English |
|
| S[17] | 40 | Male | Maryland, United States | PC | English |
|
| S[18] | 43 | Male | Illinois, USA | PC | English |
|
| S[19] | 50 | male | California, USA | pc | English | Spanish |
| S[20] | 37 | Female | North Carolina, USA | PC | English |
|
| S[21] | 55 | Female | New York | PC | English |
|
Mean Age: 50.05 (35–81)
| Subject | Age | Gender | Location | Computer | Language | Other language |
|---|---|---|---|---|---|---|
| S[1] | 41 | female | PA, USA | PC | English |
|
| S[2] | 38 | Male | Illinois, USA | PC | English |
|
| S[3] | 48 | Male | Wisconsin, USA | PC | English |
|
| S[4] | 59 | woman | US | PC | English |
|
| S[5] | 62 | Female | California, USA | PC | English |
|
| S[6] | 43 | male | Florida, USA | PC | English |
|
| S[7] | 47 | Male | Georgia, United States | PC | English |
|
| S[8] | 51 | male | NC, Bladen | PC | English |
|
| S[9] | 81 | Female | Tennessee, USA | laptop | English |
|
| S[10] | 43 | female | Michigan, United States | PC | English |
|
| S[11] | 44 | Male | Michigan, United States | PC | English |
|
| S[12] | 38 | male | NC, USA | PC | English |
|
| S[13] | 39 | Male | Kansas, United States | PC | English |
|
| S[14] | 55 | female | Florida, USA | PC | English |
|
| S[15] | 53 | Woman | Ohio, United States | Mac | English |
|
| S[16] | 49 | male | florida, USA | PC | English |
|
| S[17] | 53 | Woman | GA, United States | PC Windows | English |
|
| S[18] | 47 | Male | Indiana, USA | PC | English |
|
| S[19] | 65 | female | North Carolina, USA | PC | English |
|
| S[20] | 60 | Female | MA, USA | PC | English |
|
| S[21] | 35 | female | CA, US | Mac | English |
|
We compare the share of must vs gotta across inference conditions (a, d, i) under two preambles: an if-p conditional (cond) and a well-then discourse preamble (well).
Preamble: if-p (cond)
Takeaway: Near-categorical preference for must under if-p across all inference conditions; gotta contributes only trace amounts in a/d and none in i.
Preamble: well-then (well)
Takeaway: Must still dominates, but well-then allows a small gotta share—most notably in i—while d remains categorically must.
| Preamble | Exp | age | inference | Mean | Lower CI | Upper CI |
|---|---|---|---|---|---|---|
| prob | ||||||
| If p, ... | exp1 | 22 | abductive | 0.70 | 0.42 | 0.98 |
| If p, ... | exp1 | 22 | deductive | 0.30 | 0.02 | 0.58 |
| If p, ... | exp1 | 22 | inductive | 0.60 | 0.17 | 1.00 |
| Well, then ... | exp2 | 46 | abductive | 0.54 | 0.38 | 0.72 |
| Well, then ... | exp2 | 46 | deductive | 0.28 | 0.13 | 0.42 |
| Well, then ... | exp2 | 46 | inductive | 0.45 | 0.30 | 0.60 |
| haveto | ||||||
| If p, ... | exp1 | 19 | abductive | 0.07 | 0.00 | 0.22 |
| If p, ... | exp1 | 19 | deductive | 0.07 | 0.00 | 0.22 |
| If p, ... | exp1 | 19 | inductive | 0.29 | 0.07 | 0.50 |
| Well, then ... | exp2 | 37 | abductive | 0.07 | 0.00 | 0.17 |
| Well, then ... | exp2 | 37 | deductive | 0.29 | 0.12 | 0.45 |
| Well, then ... | exp2 | 37 | inductive | 0.10 | 0.00 | 0.20 |
| If p, ... | exp3 | 49 | abductive | 0.07 | 0.00 | 0.14 |
| If p, ... | exp3 | 49 | deductive | 0.12 | 0.01 | 0.23 |
| If p, ... | exp3 | 49 | inductive | 0.14 | 0.05 | 0.24 |
| will | ||||||
| If p, ... | exp1 | 22 | abductive | 0.00 | 0.00 | 0.00 |
| If p, ... | exp1 | 22 | deductive | 0.20 | 0.00 | 0.66 |
| If p, ... | exp1 | 22 | inductive | 0.20 | 0.00 | 0.48 |
| Well, then ... | exp2 | 46 | abductive | 0.05 | 0.00 | 0.11 |
| Well, then ... | exp2 | 46 | deductive | 0.22 | 0.08 | 0.37 |
| Well, then ... | exp2 | 46 | inductive | 0.10 | 0.00 | 0.21 |
| bare | ||||||
| If p, ... | exp1 | 21 | abductive | 0.14 | 0.00 | 0.27 |
| If p, ... | exp1 | 21 | deductive | 0.09 | 0.00 | 0.27 |
| If p, ... | exp1 | 21 | inductive | 0.14 | 0.00 | 0.27 |
| Well, then ... | exp2 | 43 | abductive | 0.22 | 0.05 | 0.40 |
| Well, then ... | exp2 | 43 | deductive | 0.47 | 0.28 | 0.67 |
| Well, then ... | exp2 | 43 | inductive | 0.31 | 0.15 | 0.46 |
| If p, ... | exp3 | 49 | abductive | 0.20 | 0.06 | 0.34 |
| If p, ... | exp3 | 49 | deductive | 0.32 | 0.15 | 0.50 |
| If p, ... | exp3 | 49 | inductive | 0.22 | 0.07 | 0.38 |
| gotta | ||||||
| If p, ... | exp4 | 46 | abductive | 0.03 | 0.00 | 0.08 |
| If p, ... | exp4 | 46 | deductive | 0.05 | 0.00 | 0.12 |
| If p, ... | exp4 | 46 | inductive | 0.00 | 0.00 | 0.00 |
| Well, then ... | exp4 | 46 | abductive | 0.05 | 0.00 | 0.12 |
| Well, then ... | exp4 | 46 | deductive | 0.00 | 0.00 | 0.00 |
| Well, then ... | exp4 | 46 | inductive | 0.08 | 0.00 | 0.19 |